Compression of video data inherently involves reduction of picture quality to increase data storage efficiency and/or reduce the required transmission bandwidth. Although the object of compression algorithms is to eliminate only imperceptible characteristics of a video frame, often times the compressed video frame includes noticeable imperfections known as artifacts. Common compression artifacts include blocking, banding, dark patches and ringing artifacts, which may be present at both low and high compression ratios.
Various methods for reducing artifacts and artifact severity are performed as post-processing operations after the compressed video data stream is decoded. One example of a post-processing technique is adaptive filtering, as described in H. S. Kong, A. Vetro, H. Sun, “Edge map guided adaptive post-filter for blocking and ringing artifacts removal”, IEEE International Symposium on Circuits and Systems (ISCAS), vol. 3, pp. 929-932, May 2004, and A. Kaup, “Reduction of ringing noise in transform image coding using a simple adaptive filter”, Electronics Letters, vol. 34, no. 22, pp. 2110-2112, October 1998. Additionally, V. Monga, N. Damera-Venkata, B. L. Evans, “Image Halftoning by Error Diffusion: A Survey of Methods for Artifact Reduction”, Journal of Electronic Imaging, 2003 offers a description of dithering. However, to ensure high picture quality and a very low incidence of artifacts, artifacts should be corrected during the compression stage of video processing.
For compression methods using a discrete cosine transform (DCT), a known method of such compressed domain processing comprises altering some of the quantized DCT coefficients. The DCT coefficients represent the power of each frequency present in a given image block. For most image blocks, after DCT transformation the majority of signal energy is carried by just a few of the low order DCT coefficients. These coefficients need to be more finely quantized than the higher order coefficients in order to avoid introducing visible artifacts. Altering a quantized DCT coefficient can either reduce or enhance the perception of compression artifacts.
B. Gunturk, Y. Altunbasak, R. M. Merserau, “Multi-frame blocking-artifact reduction for transform-coded video”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 12, no. 4, April 2002, discloses an example of altering a quantized DCT coefficient by using a projection onto a convex set approach. Alternatively, G. A. Triantafyllidis, D. Tzovras, M. G. Strintzis, “Blocking artifact detection and reduction in compressed data”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 12, no. 10, October 2002, describes a method of optimizing the quantization of a DCT coefficient by minimizing an error criterion in the transform domain. Moreover, other compression domain parameters may be altered to reduce the severity of artifacts. For instance, Ruol, U.S. Publication No. 2003/0156642 A1, discloses a method of reducing the severity of artifacts in a video stream in a two-pass encoder by adjusting the quantizer step size for macroblocks of a frame that are identified in an artifact map.
However, the known methods described above fail to address the problem of utilizing limited encoding system resources (e.g., bit budget and/or encoding time) in an efficient manner to eliminate or reduce the severity of artifacts by adjusting compressed domain parameters such as mode decisions or quantization parameters. Specifically, known methods do not prioritize video frames by considering different types of artifacts within video frames or the content of the video frames to adjust mode decisions or quantization parameters.